Quick check of the first no-genetic data demultiplex sequencing from the pilot. NPCs

library(Seurat)
library(tidyverse)
#library(DoubletFinder)
library(enrichR)
Welcome to enrichR
Checking connection ... 
Enrichr ... Connection is Live!
FlyEnrichr ... Connection is available!
WormEnrichr ... Connection is available!
YeastEnrichr ... Connection is available!
FishEnrichr ... Connection is available!
OxEnrichr ... Connection is available!
library(clustree)
Loading required package: ggraph
#library("scClassify")
#library(SingleCellExperiment)
#library("Matrix")

Read in the data

dim(seu)
[1] 33538 17520

table(seu$souporcell_assignment)

         0          1         10         11         12          2          3          4          5 
      1135       1028        426       3134       2494        207       2331        187        307 
         6          7          8          9    doublet unassigned 
       381       1192        599       1510       2069        520 

Look at the sequencing


VlnPlot(seu, features = c("nCount_RNA","nFeature_RNA"), pt.size = 0.001)

VlnPlot(seu, features = c("nFeature_RNA"), pt.size = 0.001, y.max = 500)
Warning: Removed 16577 rows containing non-finite values (`stat_ydensity()`).
Warning: Removed 16577 rows containing missing values (`geom_point()`).

table(seu$souporcell_status)

   doublet    singlet unassigned 
      1820      13603         50 
dim(seu)
[1] 33538 15473

Calculate the percent mitochondrial genes

seu$percent.MT %>% summary
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   2.578   3.541   4.201   4.591  62.044 
VlnPlot(seu, features = "percent.MT", pt.size = 0.001)

VlnPlot(seu, features = "percent.MT", pt.size = 0.001, y.max = 20)
Warning: Removed 339 rows containing non-finite values (`stat_ydensity()`).
Warning: Removed 339 rows containing missing values (`geom_point()`).

I’ll use 10% as a cutoff and check if this removes more of the low read cells. I believe this object is already the CellRanger Filtered object.

VlnPlot(seu, features = c("nCount_RNA","nFeature_RNA"), group.by = "souporcell_status")
# see most of the unassigned have low counts and features and doublets have higher counts and higher features

VlnPlot(seu, features = c("nCount_RNA"), group.by = "souporcell_status", y.max = 5000)
Warning: Removed 14766 rows containing non-finite values (`stat_ydensity()`).
Warning: Removed 14766 rows containing missing values (`geom_point()`).

VlnPlot(seu, features = c("nFeature_RNA"), group.by = "souporcell_status", y.max = 2000)
Warning: Removed 14261 rows containing non-finite values (`stat_ydensity()`).
Warning: Removed 14261 rows containing missing values (`geom_point()`).

I will filter for higher counts and lower counts

dim(seu)
[1] 33538 17520
dim(seu.ft)
[1] 33538 16864
dim(seu.ft2)
[1] 33538 15473
# this removes 1391 

VlnPlot(seu.ft2, features = c("nCount_RNA","nFeature_RNA","percent.MT"), pt.size = 0.001)


# how many of each line do we detect

table(seu.ft2$souporcell_assignment)

         0          1         10         11         12          2          3          4          5 
      1021        971        374       2913       2356         74       2165         92        232 
         6          7          8          9    doublet unassigned 
       348       1096        548       1413       1820         50 
VlnPlot(seu.ft2, features = c("nCount_RNA","nFeature_RNA","percent.MT"), group.by = "souporcell_assignment", pt.size = 0.001)

clean up


seu <- seu.ft2
rm(seu.ft2, seu.ft)

Now the workflow to make clusters

p2
Warning: Transformation introduced infinite values in continuous x-axis

Scale and get PCA



#Linear dimensionality reduction
#Choosing the number of PCs can depend on how many cells you have
seu <- ScaleData(seu)
Centering and scaling data matrix

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seu <- RunPCA(seu, assay = "RNA", npcs = 30)
PC_ 1 
Positive:  CKB, TUBB2B, MAP1B, TUBA1A, CRABP1, FZD3, TTYH1, SOX2, HSP90AA1, HMGA1 
       POU3F2, BEX1, CRMP1, KIF5C, MAP2, RFX4, DRAXIN, SOX11, FGFBP3, HES6 
       PRTG, SRGAP3, GPM6B, LIN28A, TAGLN3, SYT11, MIAT, ELAVL2, MAP6, NES 
Negative:  COL3A1, LGALS1, S100A11, LUM, DCN, SPARC, PCOLCE, B2M, COL1A1, CDH11 
       SERPINH1, APOE, IFITM3, FN1, COL5A2, COL5A1, COL6A2, RRBP1, TIMP1, SELENOM 
       COLEC12, CALD1, LAPTM4A, BGN, COL6A3, TPM1, COL1A2, EMP3, IFI16, IGFBP4 
PC_ 2 
Positive:  STMN2, DCX, TAGLN3, INA, ELAVL3, STMN4, NEFM, KLHL35, ELAVL4, ONECUT2 
       SCG3, NHLH1, DCC, SRRM4, SYT1, MLLT11, INSM1, CNTN2, NCAM1, DLL3 
       ATCAY, SOX4, CDKN1C, TFAP2B, GDAP1L1, GNG3, SSTR2, CLDN5, ELAVL2, KIF5C 
Negative:  HMGB2, CENPF, TOP2A, CKS1B, MKI67, NUSAP1, BIRC5, TUBA1B, H2AFZ, TPX2 
       CCNB1, CKS2, H2AFX, ASPM, UBE2C, MAD2L1, SMC4, DLGAP5, GTSE1, PTTG1 
       PCLAF, TUBB4B, CENPE, TYMS, CDK1, HSPD1, CCNA2, KIF20B, CDC20, KIF11 
PC_ 3 
Positive:  KRT19, KRT8, THY1, S100A10, TM4SF1, NPNT, KRT18, HAND2, MYRF, NNMT 
       DSG2, RPP25, MATN2, ASPHD1, FXYD5, GATA6, LMCD1, SLC9A3R1, KCNK6, PLP2 
       LRRN4, PITX1, CYBA, MAN1A1, KDR, BNC1, COL1A1, ANXA1, S100A16, POSTN 
Negative:  IFI44L, PRRX1, SMOC2, PTH1R, SFRP1, ZIC1, TWIST1, LRRC17, DAB2, COLEC12 
       CDC42EP5, CPE, TPM1, SLC1A3, NAV3, FOXC1, ID1, IFI44, FRZB, COL26A1 
       AKAP12, DACT2, PRRX2, NNAT, LAMA4, RND3, FN1, VCAN, TCIM, SIX1 
PC_ 4 
Positive:  TNNI1, DES, NEB, TNNT1, MYL1, KLHL41, TTN, TNNC2, CHRNA1, MYL4 
       MYOG, MYLPF, ENO3, CDH15, SMPX, ACTC1, TNNT2, MYOD1, AC020909.2, MRLN 
       IL17B, SGCA, FITM1, SYNPO2L, MYH3, TRIM55, TNNC1, ATP2A1, APOBEC2, SMYD1 
Negative:  PTN, IGFBP2, CRABP1, GJA1, ZIC2, IFI16, PDLIM1, COL11A1, VCAN, FABP5 
       TTYH1, SFRP1, IFIT3, DAB2, CLU, SERPING1, PARP14, SOX2, IFITM1, MGP 
       SMOC2, C7, SFRP2, ARHGAP29, IFI6, CDC42EP5, SLC1A3, CMBL, LIFR, FTH1 
PC_ 5 
Positive:  ASPM, CENPE, TPX2, TOP2A, UBE2C, GTSE1, MKI67, UBE2S, SGO2, DLGAP5 
       ARL6IP1, KPNA2, PLK1, NUSAP1, AURKA, CCNB1, PIF1, CENPF, NDC80, HMMR 
       KIF2C, CDK1, KIF20B, CDCA8, DEPDC1, KIF11, TTK, KIF23, CDCA2, CDCA3 
Negative:  RPL12, VIM, AL359091.1, TTYH1, HES5, NPPC, HMGA1, MCM6, MCM5, AL139246.5 
       SERF2, GSTP1, DDIT4, SFRP2, CCND1, IGFBP5, RFX4, FGFBP3, SMOC1, MGST1 
       RPL22L1, DIO3, FAM111B, MCM4, SOX2, MCM3, RPRML, HSPE1, TKTL1, HSPB1 
PCAPlot(seu)


#Assess how many PCs capture most of the information in the data 
ElbowPlot(seu, ndims = 30)

20 PCs are good now make the UMAP

Find clusters


seu <- FindNeighbors(seu, dims = 1:20, k.param = 121)
Computing nearest neighbor graph
Computing SNN
# the number of clusters is dependent on the resolution a number from 0-2. 
# Higher values make more clusters
# we include 
seu <- FindClusters(seu, resolution = c(0,0.05,0.25,0.4,0.5,0.6,1,1.5) )
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 15473
Number of edges: 2981694

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 1.0000
Number of communities: 1
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 15473
Number of edges: 2981694

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9714
Number of communities: 4
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 15473
Number of edges: 2981694

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9167
Number of communities: 6
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 15473
Number of edges: 2981694

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8917
Number of communities: 9
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 15473
Number of edges: 2981694

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8775
Number of communities: 10
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 15473
Number of edges: 2981694

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8662
Number of communities: 11
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 15473
Number of edges: 2981694

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8261
Number of communities: 14
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 15473
Number of edges: 2981694

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7905
Number of communities: 21
Elapsed time: 7 seconds
# we can visualize which cells are grouped together at different resolutions using clustree

clustree(seu, prefix = "RNA_snn_res.")

NA
NA

Visualize

DimPlot(seu, group.by = "RNA_snn_res.0.6", label = TRUE)

DimPlot(seu, group.by = "RNA_snn_res.1", label = TRUE)

Look at expression profiles of known cell type markers



da_neurons <- c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")
NPC_orStemLike <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
mature_neurons = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
excitatory_neurons = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
inhbitory_neurons = inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
astrocytes <- c("GFAP","S100B","AQP4","APOE", "SOX9","SLC1A3")
oligodendrocytes <- c("MBP","MOG","OLIG1","OLIG2","SOX10")
opc <- 
radial_glia <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
              "ITGB5","IL6ST","SLC1A3")
epithelial <- c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1")

microglia <- c("IBA1","P2RY12","P2RY13","TREM119", "GPR34","SIGLECH","TREM2",
               "CX3CR1","FCRLS","OLFML3","HEXB","TGFBR1", "SALL1","MERTK",
               "PROS1")

features_list <- c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")

short_list <- c("MKI67","SOX9","HES1","NES","DLX2","RBFOX3","MAP2","TH","CALB1","KCNJ6","SLC1A3","CD44","AQP4","S100B","OLIG2","MBP","VIM")

View on UMAP


Idents(seu) <- "RNA_snn_res.0.6"

for (i in NPC_orStemLike) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Warning in FetchData.Seurat(object = object, vars = c(dims, "ident", features),  :
  The following requested variables were not found: MASH1
Error: None of the requested features were found: MASH1 in slot data

Idents(seu) <- "RNA_snn_res.0.6"

for (i in astrocytes) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}


Idents(seu) <- "RNA_snn_res.0.6"

for (i in radial_glia) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Warning in FeaturePlot(seu, features = i, min.cutoff = "q1", max.cutoff = "q97",  :
  All cells have the same value (0) of ADGRE1.

Idents(seu) <- "RNA_snn_res.0.6"

mature_neuronsB = c("RBFOX3","SYP","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
for (i in mature_neuronsB) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}

NA
NA
NA

Dot plots and heatmaps

DotPlot(seu, features = radial_glia)+ RotatedAxis()

DotPlot(seu, features = NPC_orStemLike) + RotatedAxis()
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
  The following requested variables were not found: MASH1

DotPlot(seu, features = astrocytes) + RotatedAxis()

Find cluster markers

Idents(seu) <- "RNA_snn_res.0.6"
ClusterMarkers <- FindAllMarkers(seu, only.pos = TRUE)
Calculating cluster 0

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Calculating cluster 1

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  |++++++++++++++++++++++++++++++++++++              | 72% ~04s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~04s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~04s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~04s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~04s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=15s  
Calculating cluster 2

  |                                                  | 0 % ~calculating  
  |+                                                 | 2 % ~05s          
  |++                                                | 3 % ~05s          
  |+++                                               | 5 % ~05s          
  |++++                                              | 6 % ~05s          
  |++++                                              | 8 % ~05s          
  |+++++                                             | 9 % ~05s          
  |++++++                                            | 11% ~05s          
  |+++++++                                           | 12% ~05s          
  |+++++++                                           | 14% ~05s          
  |++++++++                                          | 15% ~05s          
  |+++++++++                                         | 17% ~05s          
  |++++++++++                                        | 18% ~05s          
  |++++++++++                                        | 20% ~05s          
  |+++++++++++                                       | 22% ~04s          
  |++++++++++++                                      | 23% ~04s          
  |+++++++++++++                                     | 25% ~04s          
  |++++++++++++++                                    | 26% ~04s          
  |++++++++++++++                                    | 28% ~04s          
  |+++++++++++++++                                   | 29% ~04s          
  |++++++++++++++++                                  | 31% ~04s          
  |+++++++++++++++++                                 | 32% ~04s          
  |+++++++++++++++++                                 | 34% ~04s          
  |++++++++++++++++++                                | 35% ~04s          
  |+++++++++++++++++++                               | 37% ~03s          
  |++++++++++++++++++++                              | 38% ~03s          
  |++++++++++++++++++++                              | 40% ~03s          
  |+++++++++++++++++++++                             | 42% ~03s          
  |++++++++++++++++++++++                            | 43% ~03s          
  |+++++++++++++++++++++++                           | 45% ~03s          
  |++++++++++++++++++++++++                          | 46% ~03s          
  |++++++++++++++++++++++++                          | 48% ~03s          
  |+++++++++++++++++++++++++                         | 49% ~03s          
  |++++++++++++++++++++++++++                        | 51% ~03s          
  |+++++++++++++++++++++++++++                       | 52% ~03s          
  |+++++++++++++++++++++++++++                       | 54% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |+++++++++++++++++++++++++++++                     | 57% ~02s          
  |++++++++++++++++++++++++++++++                    | 58% ~02s          
  |++++++++++++++++++++++++++++++                    | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 62% ~02s          
  |++++++++++++++++++++++++++++++++                  | 63% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~02s          
  |++++++++++++++++++++++++++++++++++                | 66% ~02s          
  |++++++++++++++++++++++++++++++++++                | 68% ~02s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s  
Calculating cluster 3

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~22s          
  |++                                                | 2 % ~21s          
  |++                                                | 3 % ~21s          
  |+++                                               | 5 % ~20s          
  |+++                                               | 6 % ~20s          
  |++++                                              | 7 % ~21s          
  |+++++                                             | 8 % ~21s          
  |+++++                                             | 9 % ~21s          
  |++++++                                            | 10% ~21s          
  |++++++                                            | 12% ~20s          
  |+++++++                                           | 13% ~20s          
  |+++++++                                           | 14% ~19s          
  |++++++++                                          | 15% ~19s          
  |+++++++++                                         | 16% ~19s          
  |+++++++++                                         | 17% ~18s          
  |++++++++++                                        | 19% ~18s          
  |++++++++++                                        | 20% ~18s          
  |+++++++++++                                       | 21% ~17s          
  |++++++++++++                                      | 22% ~17s          
  |++++++++++++                                      | 23% ~17s          
  |+++++++++++++                                     | 24% ~16s          
  |+++++++++++++                                     | 26% ~16s          
  |++++++++++++++                                    | 27% ~16s          
  |++++++++++++++                                    | 28% ~15s          
  |+++++++++++++++                                   | 29% ~15s          
  |++++++++++++++++                                  | 30% ~15s          
  |++++++++++++++++                                  | 31% ~15s          
  |+++++++++++++++++                                 | 33% ~14s          
  |+++++++++++++++++                                 | 34% ~14s          
  |++++++++++++++++++                                | 35% ~14s          
  |+++++++++++++++++++                               | 36% ~14s          
  |+++++++++++++++++++                               | 37% ~13s          
  |++++++++++++++++++++                              | 38% ~13s          
  |++++++++++++++++++++                              | 40% ~13s          
  |+++++++++++++++++++++                             | 41% ~13s          
  |+++++++++++++++++++++                             | 42% ~12s          
  |++++++++++++++++++++++                            | 43% ~12s          
  |+++++++++++++++++++++++                           | 44% ~12s          
  |+++++++++++++++++++++++                           | 45% ~11s          
  |++++++++++++++++++++++++                          | 47% ~11s          
  |++++++++++++++++++++++++                          | 48% ~11s          
  |+++++++++++++++++++++++++                         | 49% ~11s          
  |+++++++++++++++++++++++++                         | 50% ~10s          
  |++++++++++++++++++++++++++                        | 51% ~10s          
  |+++++++++++++++++++++++++++                       | 52% ~10s          
  |+++++++++++++++++++++++++++                       | 53% ~10s          
  |++++++++++++++++++++++++++++                      | 55% ~09s          
  |++++++++++++++++++++++++++++                      | 56% ~09s          
  |+++++++++++++++++++++++++++++                     | 57% ~09s          
  |++++++++++++++++++++++++++++++                    | 58% ~09s          
  |++++++++++++++++++++++++++++++                    | 59% ~08s          
  |+++++++++++++++++++++++++++++++                   | 60% ~08s          
  |+++++++++++++++++++++++++++++++                   | 62% ~08s          
  |++++++++++++++++++++++++++++++++                  | 63% ~08s          
  |++++++++++++++++++++++++++++++++                  | 64% ~07s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~07s          
  |++++++++++++++++++++++++++++++++++                | 66% ~07s          
  |++++++++++++++++++++++++++++++++++                | 67% ~07s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~06s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~06s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~06s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~06s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~05s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~05s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~05s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~05s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~04s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=20s  
Calculating cluster 4

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~09s          
  |++                                                | 2 % ~10s          
  |++                                                | 4 % ~10s          
  |+++                                               | 5 % ~10s          
  |++++                                              | 6 % ~10s          
  |++++                                              | 7 % ~10s          
  |+++++                                             | 8 % ~10s          
  |+++++                                             | 10% ~10s          
  |++++++                                            | 11% ~10s          
  |+++++++                                           | 12% ~09s          
  |+++++++                                           | 13% ~09s          
  |++++++++                                          | 14% ~09s          
  |++++++++                                          | 16% ~09s          
  |+++++++++                                         | 17% ~09s          
  |++++++++++                                        | 18% ~09s          
  |++++++++++                                        | 19% ~08s          
  |+++++++++++                                       | 20% ~08s          
  |+++++++++++                                       | 22% ~08s          
  |++++++++++++                                      | 23% ~08s          
  |+++++++++++++                                     | 24% ~08s          
  |+++++++++++++                                     | 25% ~08s          
  |++++++++++++++                                    | 27% ~07s          
  |++++++++++++++                                    | 28% ~07s          
  |+++++++++++++++                                   | 29% ~07s          
  |++++++++++++++++                                  | 30% ~07s          
  |++++++++++++++++                                  | 31% ~07s          
  |+++++++++++++++++                                 | 33% ~07s          
  |+++++++++++++++++                                 | 34% ~07s          
  |++++++++++++++++++                                | 35% ~07s          
  |+++++++++++++++++++                               | 36% ~06s          
  |+++++++++++++++++++                               | 37% ~06s          
  |++++++++++++++++++++                              | 39% ~06s          
  |++++++++++++++++++++                              | 40% ~06s          
  |+++++++++++++++++++++                             | 41% ~06s          
  |++++++++++++++++++++++                            | 42% ~06s          
  |++++++++++++++++++++++                            | 43% ~06s          
  |+++++++++++++++++++++++                           | 45% ~06s          
  |+++++++++++++++++++++++                           | 46% ~05s          
  |++++++++++++++++++++++++                          | 47% ~05s          
  |+++++++++++++++++++++++++                         | 48% ~05s          
  |+++++++++++++++++++++++++                         | 49% ~05s          
  |++++++++++++++++++++++++++                        | 51% ~05s          
  |++++++++++++++++++++++++++                        | 52% ~05s          
  |+++++++++++++++++++++++++++                       | 53% ~05s          
  |++++++++++++++++++++++++++++                      | 54% ~05s          
  |++++++++++++++++++++++++++++                      | 55% ~04s          
  |+++++++++++++++++++++++++++++                     | 57% ~04s          
  |+++++++++++++++++++++++++++++                     | 58% ~04s          
  |++++++++++++++++++++++++++++++                    | 59% ~04s          
  |+++++++++++++++++++++++++++++++                   | 60% ~04s          
  |+++++++++++++++++++++++++++++++                   | 61% ~04s          
  |++++++++++++++++++++++++++++++++                  | 63% ~04s          
  |++++++++++++++++++++++++++++++++                  | 64% ~04s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~03s          
  |++++++++++++++++++++++++++++++++++                | 66% ~03s          
  |++++++++++++++++++++++++++++++++++                | 67% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s  
Calculating cluster 5

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~43s          
  |++                                                | 2 % ~44s          
  |++                                                | 3 % ~43s          
  |+++                                               | 4 % ~41s          
  |+++                                               | 5 % ~40s          
  |++++                                              | 6 % ~40s          
  |++++                                              | 7 % ~39s          
  |+++++                                             | 8 % ~39s          
  |+++++                                             | 9 % ~38s          
  |++++++                                            | 11% ~37s          
  |++++++                                            | 12% ~36s          
  |+++++++                                           | 13% ~36s          
  |+++++++                                           | 14% ~35s          
  |++++++++                                          | 15% ~35s          
  |++++++++                                          | 16% ~34s          
  |+++++++++                                         | 17% ~34s          
  |+++++++++                                         | 18% ~33s          
  |++++++++++                                        | 19% ~33s          
  |++++++++++                                        | 20% ~33s          
  |+++++++++++                                       | 21% ~32s          
  |++++++++++++                                      | 22% ~32s          
  |++++++++++++                                      | 23% ~31s          
  |+++++++++++++                                     | 24% ~31s          
  |+++++++++++++                                     | 25% ~30s          
  |++++++++++++++                                    | 26% ~30s          
  |++++++++++++++                                    | 27% ~29s          
  |+++++++++++++++                                   | 28% ~29s          
  |+++++++++++++++                                   | 29% ~29s          
  |++++++++++++++++                                  | 31% ~28s          
  |++++++++++++++++                                  | 32% ~28s          
  |+++++++++++++++++                                 | 33% ~27s          
  |+++++++++++++++++                                 | 34% ~27s          
  |++++++++++++++++++                                | 35% ~27s          
  |++++++++++++++++++                                | 36% ~26s          
  |+++++++++++++++++++                               | 37% ~26s          
  |+++++++++++++++++++                               | 38% ~25s          
  |++++++++++++++++++++                              | 39% ~25s          
  |++++++++++++++++++++                              | 40% ~24s          
  |+++++++++++++++++++++                             | 41% ~24s          
  |++++++++++++++++++++++                            | 42% ~24s          
  |++++++++++++++++++++++                            | 43% ~23s          
  |+++++++++++++++++++++++                           | 44% ~23s          
  |+++++++++++++++++++++++                           | 45% ~22s          
  |++++++++++++++++++++++++                          | 46% ~22s          
  |++++++++++++++++++++++++                          | 47% ~21s          
  |+++++++++++++++++++++++++                         | 48% ~21s          
  |+++++++++++++++++++++++++                         | 49% ~20s          
  |++++++++++++++++++++++++++                        | 51% ~20s          
  |++++++++++++++++++++++++++                        | 52% ~20s          
  |+++++++++++++++++++++++++++                       | 53% ~19s          
  |+++++++++++++++++++++++++++                       | 54% ~19s          
  |++++++++++++++++++++++++++++                      | 55% ~18s          
  |++++++++++++++++++++++++++++                      | 56% ~18s          
  |+++++++++++++++++++++++++++++                     | 57% ~17s          
  |+++++++++++++++++++++++++++++                     | 58% ~17s          
  |++++++++++++++++++++++++++++++                    | 59% ~17s          
  |++++++++++++++++++++++++++++++                    | 60% ~16s          
  |+++++++++++++++++++++++++++++++                   | 61% ~16s          
  |++++++++++++++++++++++++++++++++                  | 62% ~15s          
  |++++++++++++++++++++++++++++++++                  | 63% ~15s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~14s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~14s          
  |++++++++++++++++++++++++++++++++++                | 66% ~14s          
  |++++++++++++++++++++++++++++++++++                | 67% ~13s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~13s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~12s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~12s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~11s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~11s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~11s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~10s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~10s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~09s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~09s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~08s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~08s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~08s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=40s  
Calculating cluster 6

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~10s          
  |++                                                | 2 % ~10s          
  |++                                                | 3 % ~10s          
  |+++                                               | 5 % ~09s          
  |+++                                               | 6 % ~09s          
  |++++                                              | 7 % ~09s          
  |++++                                              | 8 % ~09s          
  |+++++                                             | 9 % ~09s          
  |++++++                                            | 10% ~08s          
  |++++++                                            | 11% ~08s          
  |+++++++                                           | 12% ~08s          
  |+++++++                                           | 14% ~08s          
  |++++++++                                          | 15% ~08s          
  |++++++++                                          | 16% ~08s          
  |+++++++++                                         | 17% ~08s          
  |++++++++++                                        | 18% ~08s          
  |++++++++++                                        | 19% ~08s          
  |+++++++++++                                       | 20% ~08s          
  |+++++++++++                                       | 22% ~07s          
  |++++++++++++                                      | 23% ~07s          
  |++++++++++++                                      | 24% ~07s          
  |+++++++++++++                                     | 25% ~07s          
  |++++++++++++++                                    | 26% ~07s          
  |++++++++++++++                                    | 27% ~07s          
  |+++++++++++++++                                   | 28% ~07s          
  |+++++++++++++++                                   | 30% ~07s          
  |++++++++++++++++                                  | 31% ~07s          
  |++++++++++++++++                                  | 32% ~07s          
  |+++++++++++++++++                                 | 33% ~06s          
  |++++++++++++++++++                                | 34% ~06s          
  |++++++++++++++++++                                | 35% ~06s          
  |+++++++++++++++++++                               | 36% ~06s          
  |+++++++++++++++++++                               | 38% ~06s          
  |++++++++++++++++++++                              | 39% ~06s          
  |++++++++++++++++++++                              | 40% ~06s          
  |+++++++++++++++++++++                             | 41% ~06s          
  |++++++++++++++++++++++                            | 42% ~06s          
  |++++++++++++++++++++++                            | 43% ~06s          
  |+++++++++++++++++++++++                           | 44% ~05s          
  |+++++++++++++++++++++++                           | 45% ~05s          
  |++++++++++++++++++++++++                          | 47% ~05s          
  |++++++++++++++++++++++++                          | 48% ~05s          
  |+++++++++++++++++++++++++                         | 49% ~05s          
  |+++++++++++++++++++++++++                         | 50% ~05s          
  |++++++++++++++++++++++++++                        | 51% ~05s          
  |+++++++++++++++++++++++++++                       | 52% ~05s          
  |+++++++++++++++++++++++++++                       | 53% ~05s          
  |++++++++++++++++++++++++++++                      | 55% ~04s          
  |++++++++++++++++++++++++++++                      | 56% ~04s          
  |+++++++++++++++++++++++++++++                     | 57% ~04s          
  |+++++++++++++++++++++++++++++                     | 58% ~04s          
  |++++++++++++++++++++++++++++++                    | 59% ~04s          
  |+++++++++++++++++++++++++++++++                   | 60% ~04s          
  |+++++++++++++++++++++++++++++++                   | 61% ~04s          
  |++++++++++++++++++++++++++++++++                  | 62% ~04s          
  |++++++++++++++++++++++++++++++++                  | 64% ~04s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~03s          
  |++++++++++++++++++++++++++++++++++                | 67% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s  
Calculating cluster 7

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~15s          
  |++                                                | 2 % ~14s          
  |++                                                | 3 % ~30s          
  |+++                                               | 4 % ~25s          
  |+++                                               | 5 % ~22s          
  |++++                                              | 6 % ~19s          
  |++++                                              | 7 % ~18s          
  |+++++                                             | 8 % ~16s          
  |+++++                                             | 9 % ~15s          
  |++++++                                            | 11% ~14s          
  |++++++                                            | 12% ~14s          
  |+++++++                                           | 13% ~13s          
  |+++++++                                           | 14% ~13s          
  |++++++++                                          | 15% ~12s          
  |++++++++                                          | 16% ~12s          
  |+++++++++                                         | 17% ~11s          
  |+++++++++                                         | 18% ~11s          
  |++++++++++                                        | 19% ~11s          
  |++++++++++                                        | 20% ~11s          
  |+++++++++++                                       | 21% ~10s          
  |++++++++++++                                      | 22% ~10s          
  |++++++++++++                                      | 23% ~10s          
  |+++++++++++++                                     | 24% ~10s          
  |+++++++++++++                                     | 25% ~09s          
  |++++++++++++++                                    | 26% ~09s          
  |++++++++++++++                                    | 27% ~09s          
  |+++++++++++++++                                   | 28% ~09s          
  |+++++++++++++++                                   | 29% ~09s          
  |++++++++++++++++                                  | 31% ~08s          
  |++++++++++++++++                                  | 32% ~08s          
  |+++++++++++++++++                                 | 33% ~08s          
  |+++++++++++++++++                                 | 34% ~08s          
  |++++++++++++++++++                                | 35% ~08s          
  |++++++++++++++++++                                | 36% ~08s          
  |+++++++++++++++++++                               | 37% ~08s          
  |+++++++++++++++++++                               | 38% ~07s          
  |++++++++++++++++++++                              | 39% ~07s          
  |++++++++++++++++++++                              | 40% ~07s          
  |+++++++++++++++++++++                             | 41% ~07s          
  |++++++++++++++++++++++                            | 42% ~07s          
  |++++++++++++++++++++++                            | 43% ~07s          
  |+++++++++++++++++++++++                           | 44% ~07s          
  |+++++++++++++++++++++++                           | 45% ~06s          
  |++++++++++++++++++++++++                          | 46% ~06s          
  |++++++++++++++++++++++++                          | 47% ~06s          
  |+++++++++++++++++++++++++                         | 48% ~06s          
  |+++++++++++++++++++++++++                         | 49% ~06s          
  |++++++++++++++++++++++++++                        | 51% ~06s          
  |++++++++++++++++++++++++++                        | 52% ~06s          
  |+++++++++++++++++++++++++++                       | 53% ~06s          
  |+++++++++++++++++++++++++++                       | 54% ~05s          
  |++++++++++++++++++++++++++++                      | 55% ~05s          
  |++++++++++++++++++++++++++++                      | 56% ~05s          
  |+++++++++++++++++++++++++++++                     | 57% ~05s          
  |+++++++++++++++++++++++++++++                     | 58% ~05s          
  |++++++++++++++++++++++++++++++                    | 59% ~05s          
  |++++++++++++++++++++++++++++++                    | 60% ~05s          
  |+++++++++++++++++++++++++++++++                   | 61% ~04s          
  |++++++++++++++++++++++++++++++++                  | 62% ~04s          
  |++++++++++++++++++++++++++++++++                  | 63% ~04s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~04s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~04s          
  |++++++++++++++++++++++++++++++++++                | 66% ~04s          
  |++++++++++++++++++++++++++++++++++                | 67% ~04s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~04s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~03s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s  
Calculating cluster 8

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~16s          
  |++                                                | 2 % ~17s          
  |++                                                | 3 % ~17s          
  |+++                                               | 4 % ~16s          
  |+++                                               | 6 % ~16s          
  |++++                                              | 7 % ~16s          
  |++++                                              | 8 % ~16s          
  |+++++                                             | 9 % ~15s          
  |+++++                                             | 10% ~15s          
  |++++++                                            | 11% ~15s          
  |+++++++                                           | 12% ~15s          
  |+++++++                                           | 13% ~15s          
  |++++++++                                          | 14% ~15s          
  |++++++++                                          | 16% ~15s          
  |+++++++++                                         | 17% ~14s          
  |+++++++++                                         | 18% ~14s          
  |++++++++++                                        | 19% ~14s          
  |++++++++++                                        | 20% ~14s          
  |+++++++++++                                       | 21% ~14s          
  |++++++++++++                                      | 22% ~13s          
  |++++++++++++                                      | 23% ~13s          
  |+++++++++++++                                     | 24% ~13s          
  |+++++++++++++                                     | 26% ~13s          
  |++++++++++++++                                    | 27% ~13s          
  |++++++++++++++                                    | 28% ~12s          
  |+++++++++++++++                                   | 29% ~12s          
  |+++++++++++++++                                   | 30% ~12s          
  |++++++++++++++++                                  | 31% ~12s          
  |+++++++++++++++++                                 | 32% ~12s          
  |+++++++++++++++++                                 | 33% ~11s          
  |++++++++++++++++++                                | 34% ~11s          
  |++++++++++++++++++                                | 36% ~11s          
  |+++++++++++++++++++                               | 37% ~11s          
  |+++++++++++++++++++                               | 38% ~11s          
  |++++++++++++++++++++                              | 39% ~10s          
  |++++++++++++++++++++                              | 40% ~10s          
  |+++++++++++++++++++++                             | 41% ~10s          
  |++++++++++++++++++++++                            | 42% ~10s          
  |++++++++++++++++++++++                            | 43% ~10s          
  |+++++++++++++++++++++++                           | 44% ~10s          
  |+++++++++++++++++++++++                           | 46% ~09s          
  |++++++++++++++++++++++++                          | 47% ~09s          
  |++++++++++++++++++++++++                          | 48% ~09s          
  |+++++++++++++++++++++++++                         | 49% ~09s          
  |+++++++++++++++++++++++++                         | 50% ~09s          
  |++++++++++++++++++++++++++                        | 51% ~08s          
  |+++++++++++++++++++++++++++                       | 52% ~08s          
  |+++++++++++++++++++++++++++                       | 53% ~08s          
  |++++++++++++++++++++++++++++                      | 54% ~08s          
  |++++++++++++++++++++++++++++                      | 56% ~08s          
  |+++++++++++++++++++++++++++++                     | 57% ~07s          
  |+++++++++++++++++++++++++++++                     | 58% ~07s          
  |++++++++++++++++++++++++++++++                    | 59% ~07s          
  |++++++++++++++++++++++++++++++                    | 60% ~07s          
  |+++++++++++++++++++++++++++++++                   | 61% ~07s          
  |++++++++++++++++++++++++++++++++                  | 62% ~06s          
  |++++++++++++++++++++++++++++++++                  | 63% ~06s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~06s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~06s          
  |++++++++++++++++++++++++++++++++++                | 67% ~06s          
  |++++++++++++++++++++++++++++++++++                | 68% ~06s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~05s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~05s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~05s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~05s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~05s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~04s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~04s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~04s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~04s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~04s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=17s  
Calculating cluster 9

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~10s          
  |++                                                | 2 % ~08s          
  |++                                                | 4 % ~08s          
  |+++                                               | 5 % ~08s          
  |++++                                              | 6 % ~08s          
  |++++                                              | 8 % ~08s          
  |+++++                                             | 9 % ~07s          
  |+++++                                             | 10% ~07s          
  |++++++                                            | 11% ~07s          
  |+++++++                                           | 12% ~07s          
  |+++++++                                           | 14% ~07s          
  |++++++++                                          | 15% ~07s          
  |+++++++++                                         | 16% ~06s          
  |+++++++++                                         | 18% ~06s          
  |++++++++++                                        | 19% ~06s          
  |++++++++++                                        | 20% ~06s          
  |+++++++++++                                       | 21% ~06s          
  |++++++++++++                                      | 22% ~06s          
  |++++++++++++                                      | 24% ~06s          
  |+++++++++++++                                     | 25% ~06s          
  |++++++++++++++                                    | 26% ~06s          
  |++++++++++++++                                    | 28% ~06s          
  |+++++++++++++++                                   | 29% ~05s          
  |+++++++++++++++                                   | 30% ~05s          
  |++++++++++++++++                                  | 31% ~05s          
  |+++++++++++++++++                                 | 32% ~05s          
  |+++++++++++++++++                                 | 34% ~05s          
  |++++++++++++++++++                                | 35% ~05s          
  |+++++++++++++++++++                               | 36% ~05s          
  |+++++++++++++++++++                               | 38% ~05s          
  |++++++++++++++++++++                              | 39% ~05s          
  |++++++++++++++++++++                              | 40% ~05s          
  |+++++++++++++++++++++                             | 41% ~05s          
  |++++++++++++++++++++++                            | 42% ~05s          
  |++++++++++++++++++++++                            | 44% ~04s          
  |+++++++++++++++++++++++                           | 45% ~04s          
  |++++++++++++++++++++++++                          | 46% ~04s          
  |++++++++++++++++++++++++                          | 48% ~04s          
  |+++++++++++++++++++++++++                         | 49% ~04s          
  |+++++++++++++++++++++++++                         | 50% ~04s          
  |++++++++++++++++++++++++++                        | 51% ~04s          
  |+++++++++++++++++++++++++++                       | 52% ~04s          
  |+++++++++++++++++++++++++++                       | 54% ~04s          
  |++++++++++++++++++++++++++++                      | 55% ~04s          
  |+++++++++++++++++++++++++++++                     | 56% ~04s          
  |+++++++++++++++++++++++++++++                     | 58% ~03s          
  |++++++++++++++++++++++++++++++                    | 59% ~03s          
  |++++++++++++++++++++++++++++++                    | 60% ~03s          
  |+++++++++++++++++++++++++++++++                   | 61% ~03s          
  |++++++++++++++++++++++++++++++++                  | 62% ~03s          
  |++++++++++++++++++++++++++++++++                  | 64% ~03s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~03s          
  |++++++++++++++++++++++++++++++++++                | 66% ~03s          
  |++++++++++++++++++++++++++++++++++                | 68% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~02s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~02s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~02s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~02s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s  
Calculating cluster 10

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~26s          
  |++                                                | 2 % ~28s          
  |++                                                | 3 % ~26s          
  |+++                                               | 4 % ~26s          
  |+++                                               | 5 % ~25s          
  |++++                                              | 6 % ~24s          
  |++++                                              | 7 % ~24s          
  |+++++                                             | 9 % ~24s          
  |+++++                                             | 10% ~23s          
  |++++++                                            | 11% ~23s          
  |++++++                                            | 12% ~22s          
  |+++++++                                           | 13% ~22s          
  |+++++++                                           | 14% ~22s          
  |++++++++                                          | 15% ~22s          
  |++++++++                                          | 16% ~22s          
  |+++++++++                                         | 17% ~22s          
  |++++++++++                                        | 18% ~21s          
  |++++++++++                                        | 19% ~21s          
  |+++++++++++                                       | 20% ~21s          
  |+++++++++++                                       | 21% ~21s          
  |++++++++++++                                      | 22% ~21s          
  |++++++++++++                                      | 23% ~21s          
  |+++++++++++++                                     | 24% ~20s          
  |+++++++++++++                                     | 26% ~20s          
  |++++++++++++++                                    | 27% ~20s          
  |++++++++++++++                                    | 28% ~21s          
  |+++++++++++++++                                   | 29% ~21s          
  |+++++++++++++++                                   | 30% ~20s          
  |++++++++++++++++                                  | 31% ~20s          
  |++++++++++++++++                                  | 32% ~20s          
  |+++++++++++++++++                                 | 33% ~19s          
  |++++++++++++++++++                                | 34% ~19s          
  |++++++++++++++++++                                | 35% ~19s          
  |+++++++++++++++++++                               | 36% ~18s          
  |+++++++++++++++++++                               | 37% ~18s          
  |++++++++++++++++++++                              | 38% ~18s          
  |++++++++++++++++++++                              | 39% ~17s          
  |+++++++++++++++++++++                             | 40% ~17s          
  |+++++++++++++++++++++                             | 41% ~17s          
  |++++++++++++++++++++++                            | 43% ~16s          
  |++++++++++++++++++++++                            | 44% ~16s          
  |+++++++++++++++++++++++                           | 45% ~16s          
  |+++++++++++++++++++++++                           | 46% ~15s          
  |++++++++++++++++++++++++                          | 47% ~15s          
  |++++++++++++++++++++++++                          | 48% ~15s          
  |+++++++++++++++++++++++++                         | 49% ~14s          
  |+++++++++++++++++++++++++                         | 50% ~14s          
  |++++++++++++++++++++++++++                        | 51% ~14s          
  |+++++++++++++++++++++++++++                       | 52% ~14s          
  |+++++++++++++++++++++++++++                       | 53% ~13s          
  |++++++++++++++++++++++++++++                      | 54% ~13s          
  |++++++++++++++++++++++++++++                      | 55% ~13s          
  |+++++++++++++++++++++++++++++                     | 56% ~12s          
  |+++++++++++++++++++++++++++++                     | 57% ~12s          
  |++++++++++++++++++++++++++++++                    | 59% ~12s          
  |++++++++++++++++++++++++++++++                    | 60% ~11s          
  |+++++++++++++++++++++++++++++++                   | 61% ~11s          
  |+++++++++++++++++++++++++++++++                   | 62% ~11s          
  |++++++++++++++++++++++++++++++++                  | 63% ~10s          
  |++++++++++++++++++++++++++++++++                  | 64% ~10s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~10s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~09s          
  |++++++++++++++++++++++++++++++++++                | 67% ~09s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~09s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~09s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~08s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~08s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~08s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~07s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~07s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~07s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~07s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~06s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~06s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=27s  

Have a look at the top cluster markers

head(ClusterMarkers)
top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt =avg_log2FC)

top2 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=2, wt =avg_log2FC)
DoHeatmap(seu, features = top5$gene, size = 3, angle = 90, group.by = "RNA_snn_res.0.6")


DoHeatmap(seu, features = top2$gene, size = 3, angle = 90, group.by = "RNA_snn_res.0.6")

Now use EnrichR to check cell type libraries

setEnrichrSite("Enrichr") # Human genes
Connection changed to https://maayanlab.cloud/Enrichr/
Connection is Live!
# list of all the databases

dbs <- listEnrichrDbs()

# this will list the possible libraries
dbs

# select libraries with cell types
db <- c('CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')

# function for a quick look
checkCelltypes <- function(cluster_num = 0){
  clusterX <- ClusterMarkers %>% filter(cluster == cluster_num & avg_log2FC > 0.25)
  genes <- clusterX$gene
  # the cell type libraries
  # get the results for each library
  clusterX.cell <- enrichr(genes, databases = db)
  # visualize the results
print(plotEnrich(clusterX.cell[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'CellMarker_Augmented_2021'))
print(plotEnrich(clusterX.cell[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'Azimuth_Cell_Types_2021'))

}

Check each cluster

cluster0 <- checkCelltypes(cluster_num = 0)
Uploading data to Enrichr... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.

cluster1 <- checkCelltypes(cluster_num = 1)
Uploading data to Enrichr... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.

cluster2 <- checkCelltypes(cluster_num = 2)
Uploading data to Enrichr... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.

cluster3 <- checkCelltypes(cluster_num = 3)
Uploading data to Enrichr... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.

cluster4 <- checkCelltypes(cluster_num = 4)
Uploading data to Enrichr... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.

cluster5 <- checkCelltypes(cluster_num = 5)
Uploading data to Enrichr... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.

cluster6 <- checkCelltypes(cluster_num = 6)
Uploading data to Enrichr... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.

cluster7 <- checkCelltypes(cluster_num = 7)
Uploading data to Enrichr... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.

cluster8 <- checkCelltypes(cluster_num = 8)
Uploading data to Enrichr... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.

cluster9 <- checkCelltypes(cluster_num = 9)
Uploading data to Enrichr... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.

cluster10 <- checkCelltypes(cluster_num = 10)
Uploading data to Enrichr... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.

Add cell type annotations

See cell counts for each line and cell type


Idents(seu) <- "souporcell_status"
table(seu$souporcell_status)

   doublet    singlet unassigned 
      1820      13603         50 
singlet <- subset(seu, idents = "singlet")
VlnPlot(singlet, features = "nFeature_RNA", group.by = "souporcell_assignment")


Idents(singlet)<- "MainCelltypes"
DimPlot(singlet, group.by = "souporcell_assignment", label = TRUE)

We might need to separate and harmonize each cell type.

for (i in 1:length(sublist)){
  sublist[[i]] <- NormalizeData(sublist[[i]], verbose = FALSE)
  sublist[[i]] <- FindVariableFeatures(sublist[[i]], selection.method = "vst")
}
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
integrated_seurat <- IntegrateData(anchorset = int.anchors,  dims = 1:30, k.weight = 72)
Merging dataset 12 into 4
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 5 into 4 12
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 11 into 9
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 8 into 7
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 6 into 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 10 into 3
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 2 into 3 10
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 9 11 into 7 8
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 3 10 2 into 1 6
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 4 12 5 into 7 8 9 11
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 7 8 9 11 4 12 5 into 1 6 3 10 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
integrated_seurat <- RunUMAP(integrated_seurat, reduction = "pca", dims = 1:20, n.neighbors = 81)
16:42:59 UMAP embedding parameters a = 0.9922 b = 1.112
16:42:59 Read 16442 rows and found 20 numeric columns
16:42:59 Using Annoy for neighbor search, n_neighbors = 81
16:42:59 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:43:01 Writing NN index file to temp file /var/folders/k4/khtkczkd5tn732ftjpwgtr240000gn/T//Rtmp0peLrn/file761314265441
16:43:01 Searching Annoy index using 1 thread, search_k = 8100
16:43:12 Annoy recall = 100%
16:43:15 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 81
16:43:20 Initializing from normalized Laplacian + noise (using irlba)
16:43:21 Commencing optimization for 200 epochs, with 1655996 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:43:39 Optimization finished
output_path <- "/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/"

saveRDS(integrated_seurat,paste(output_path,"IntegratedSeuratGraphs.RDS",sep = ""))

integrated_seurat <- FindNeighbors(integrated_seurat, dims = 1:20, k.param = 81)
Computing nearest neighbor graph
Computing SNN
integrated_seurat <- FindClusters(integrated_seurat, resolution = c(0.6,1) )
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 16442
Number of edges: 2500751

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8712
Number of communities: 13
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 16442
Number of edges: 2500751

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8310
Number of communities: 16
Elapsed time: 8 seconds
DimPlot(integrated_seurat, group.by = "orig.ident")

#integrated_seurat$integrated_snn_res.0.6
DimPlot(integrated_seurat, group.by = "integrated_snn_res.0.6", label=TRUE)

DimPlot(integrated_seurat, group.by = "integrated_snn_res.1", label = TRUE)

Now cells per cluster

table(integrated_seurat$integrated_snn_res.0.6, integrated_seurat$orig.ident)
    
        0    1   10   11   12    3    4    5    6    7    8    9
  0   215  124   69 1160  349  467    6   26   35  191   68  224
  1    82  122   36  994  249  173    1    8   23  156   65  209
  2    86  103   21  728  211  360   10   17   23   86   90  177
  3   102   91   43  696  290  224    9   31   31  122   71  133
  4   155  350   27   12  430   36   21   45  115   52  124  378
  5    79   82   44  670  222  244    3   13   31  103   37  132
  6    90    8   24  648   37  134   12   12   10  151   12    5
  7   108   10   21  624   23  104    4    8    5   95   18   13
  8    82   30   35  226   83  176    3    7   21   97   23   28
  9    10   21   45    4  263   30   10   33   45   33   14   21
  10    6   28    9   52   80   29    6   18    9    8   18   36
  11    1    0    0    6   63  182    4    3    0    0    0    1
  12    5    2    0    6   56    6    3   11    0    2    8   56
table(integrated_seurat$integrated_snn_res.1, integrated_seurat$orig.ident)
    
        0    1   10   11   12    3    4    5    6    7    8    9
  0   185  114   63 1022  304  424    5   21   33  172   54  202
  1   131   95   49  816  316  259   10   33   32  140   82  147
  2    86  101   22  712  211  357   10   18   22   84   94  177
  3    79   85   44  672  221  247    2   13   30  104   38  132
  4    48  109   17  606  205  147    1    7   17   56   49  205
  5    90    8   24  648   37  134   12   12   10  151   12    5
  6   108   10   21  632   23  105    5    8    5   95   19   13
  7     7  169    2   10  352    7   12   31   41    6   45  212
  8   148  188   25    4   94   29    9   16   75   47   81  175
  9    35   11   18  402   46   24    0    2    6  100   11    4
  10   10   21   45    4  262   30   10   32   47   33   14   19
  11   58   10   29   68   21  146    1    5   14   70    5    7
  12   24   20    6  162   62   29    2    2    7   28   18   21
  13    6   28    9   56   82   29    6   18    9    8   18   36
  14    1    0    0    6   63  192    4    3    0    0    0    1
  15    5    2    0    6   57    6    3   11    0    2    8   57

Which cells match to which subject?

From Michaels correlation:

5+5
[1] 10

unique(seu$Genotype_ID)
 [1] "28_CH79DIVUZHD100462"          "22_CH47DIVUZHD100456"         
 [3] "4_CH96GSAUZHD100576"           "19_CH31DIVUZHD100453"         
 [5] "unassigned"                    "29_CH52DIVUZHD100463"         
 [7] "saliva_mr-023__saliva_mr-023_" "23_CH20DIVUZHD100457"         
 [9] "32_CH97GSAUZHD100946"          "3_CH26GSAUZHD100575"          

Now make a table of the assigned cell counts

table(seu$souporcell_assignment, seu$Genotype_ID)
    
     19_CH31DIVUZHD100453 22_CH47DIVUZHD100456 23_CH20DIVUZHD100457
  0                     0                 1021                    0
  1                     0                    0                  971
  10                    0                    0                    0
  11                    0                    0                    0
  12                 2356                    0                    0
  3                     0                    0                    0
  4                     0                    0                    0
  5                     0                    0                    0
  6                     0                    0                    0
  7                     0                    0                    0
  8                     0                    0                    0
  9                     0                    0                    0
    
     28_CH79DIVUZHD100462 29_CH52DIVUZHD100463 3_CH26GSAUZHD100575
  0                     0                    0                   0
  1                     0                    0                   0
  10                    0                    0                   0
  11                 5826                    0                   0
  12                    0                    0                   0
  3                     0                    0                   0
  4                     0                    0                   0
  5                     0                    0                   0
  6                     0                    0                 348
  7                     0                 1096                   0
  8                     0                    0                   0
  9                     0                    0                   0
    
     32_CH97GSAUZHD100946 4_CH96GSAUZHD100576 saliva_mr-023__saliva_mr-023_
  0                     0                   0                             0
  1                     0                   0                             0
  10                  374                   0                             0
  11                    0                   0                             0
  12                    0                   0                             0
  3                     0                2165                             0
  4                     0                   0                             0
  5                     0                   0                             0
  6                     0                   0                             0
  7                     0                   0                             0
  8                     0                   0                             0
  9                     0                   0                          1413
    
     unassigned
  0           0
  1           0
  10          0
  11          0
  12          0
  3           0
  4          92
  5         232
  6           0
  7           0
  8         548
  9           0
---
title: "R Notebook"
output: html_notebook
---

Quick check of the first no-genetic data demultiplex sequencing from the pilot.
NPCs

```{r}
library(Seurat)
library(tidyverse)
#library(DoubletFinder)
library(enrichR)
library(clustree)
#library("scClassify")
#library(SingleCellExperiment)
#library("Matrix")


```
Read in the data

```{r}
seu <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/seu_souporcell_ADHD.rds")
colnames(seu@meta.data)

unique(seu$orig.ident)
unique(seu$souporcell_assignment)
unique(seu$souporcell_status)
dim(seu)


```

```{r}

table(seu$souporcell_assignment)

```

Look at the sequencing

```{r}

VlnPlot(seu, features = c("nCount_RNA","nFeature_RNA"), pt.size = 0.001)
VlnPlot(seu, features = c("nFeature_RNA"), pt.size = 0.001, y.max = 500)

```

```{r}

table(seu$souporcell_status)
dim(seu)


```




Calculate the percent mitochondrial genes
```{r}
seu <- PercentageFeatureSet(seu, pattern = "^MT-", col.name = "percent.MT")
seu$percent.MT %>% summary

VlnPlot(seu, features = "percent.MT", pt.size = 0.001)
VlnPlot(seu, features = "percent.MT", pt.size = 0.001, y.max = 20)


```
I'll use 10% as a cutoff and check if this removes more of the low read cells. I believe this object is already the CellRanger Filtered object. 

```{r}
#Remove any cells with more than 10% mitochondrial counts
seu.ft <- subset(seu, percent.MT < 10)
dim(seu)
dim(seu.ft)

# removed 656 cells
VlnPlot(seu.ft, features = c("nCount_RNA","nFeature_RNA","percent.MT"), pt.size = 0.001)


```

```{r}
# have a look at how the counts and RNA are distributed across single, doublet, unassigned

VlnPlot(seu, features = c("nCount_RNA","nFeature_RNA"), group.by = "souporcell_status")
# see most of the unassigned have low counts and features and doublets have higher counts and higher features

VlnPlot(seu, features = c("nCount_RNA"), group.by = "souporcell_status", y.max = 5000)
VlnPlot(seu, features = c("nFeature_RNA"), group.by = "souporcell_status", y.max = 2000)

```
I will filter for higher counts and lower counts


```{r}
# Counts > 2000 to get ride of unassigned
# RNA > 300
# doublets we might just remove by the assignment or use doublet finder later

seu.ft2 <- subset(seu.ft, nCount_RNA > 2000 & nFeature_RNA > 300)
dim(seu)
dim(seu.ft)
dim(seu.ft2)
# this removes 1391 

VlnPlot(seu.ft2, features = c("nCount_RNA","nFeature_RNA","percent.MT"), pt.size = 0.001)

# how many of each line do we detect

table(seu.ft2$souporcell_assignment)
VlnPlot(seu.ft2, features = c("nCount_RNA","nFeature_RNA","percent.MT"), group.by = "souporcell_assignment", pt.size = 0.001)


```
clean up

```{r}

seu <- seu.ft2
rm(seu.ft2, seu.ft)

```

Now the workflow to make clusters
```{r}
seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2500)

var  <- VariableFeatures(seu)
top10 <- var[1:10]

p1 <- VariableFeaturePlot(seu) 
p2 <- LabelPoints(plot = p1, points = top10, repel = TRUE)
p2



```
Scale and get PCA

```{r}


#Linear dimensionality reduction
#Choosing the number of PCs can depend on how many cells you have
seu <- ScaleData(seu)
seu <- RunPCA(seu, assay = "RNA", npcs = 30)

PCAPlot(seu)

#Assess how many PCs capture most of the information in the data 
ElbowPlot(seu, ndims = 30)

```

20 PCs are good now make the UMAP

```{r}

seu <- RunUMAP(seu, dims = 1:20, n.neighbors = 121)
DimPlot(seu, group.by = "orig.ident")

```

Find clusters

```{r}

seu <- FindNeighbors(seu, dims = 1:20, k.param = 121)
# the number of clusters is dependent on the resolution a number from 0-2. 
# Higher values make more clusters
# we include 
seu <- FindClusters(seu, resolution = c(0,0.05,0.25,0.4,0.5,0.6,1,1.5) )

# we can visualize which cells are grouped together at different resolutions using clustree

clustree(seu, prefix = "RNA_snn_res.")

# 0.6 looks good to annotate.  Each cluster is splitting apart up to this point and then the cells start merging and changing clusters.

```
Visualize

```{r}
DimPlot(seu, group.by = "RNA_snn_res.0.6", label = TRUE)
DimPlot(seu, group.by = "RNA_snn_res.1", label = TRUE)
# clustering could be improved

```

Look at expression profiles of known cell type markers

```{r}


da_neurons <- c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")
NPC_orStemLike <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
mature_neurons = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
excitatory_neurons = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
inhbitory_neurons = inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
astrocytes <- c("GFAP","S100B","AQP4","APOE", "SOX9","SLC1A3")
oligodendrocytes <- c("MBP","MOG","OLIG1","OLIG2","SOX10")
opc <- 
radial_glia <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
              "ITGB5","IL6ST","SLC1A3")
epithelial <- c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1")

microglia <- c("IBA1","P2RY12","P2RY13","TREM119", "GPR34","SIGLECH","TREM2",
               "CX3CR1","FCRLS","OLFML3","HEXB","TGFBR1", "SALL1","MERTK",
               "PROS1")

features_list <- c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")

short_list <- c("MKI67","SOX9","HES1","NES","DLX2","RBFOX3","MAP2","TH","CALB1","KCNJ6","SLC1A3","CD44","AQP4","S100B","OLIG2","MBP","VIM")



```

View on UMAP

```{r}

Idents(seu) <- "RNA_snn_res.0.6"

for (i in NPC_orStemLike) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}

```

```{r}
Idents(seu) <- "RNA_snn_res.0.6"

for (i in astrocytes) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}

```

```{r}

Idents(seu) <- "RNA_snn_res.0.6"

for (i in radial_glia) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}

```

```{r}
Idents(seu) <- "RNA_snn_res.0.6"

mature_neuronsB = c("RBFOX3","SYP","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
for (i in mature_neuronsB) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}



```


Dot plots and heatmaps

```{r}
DotPlot(seu, features = radial_glia)+ RotatedAxis()
DotPlot(seu, features = NPC_orStemLike) + RotatedAxis()
DotPlot(seu, features = astrocytes) + RotatedAxis()

```

Find cluster markers

```{r}
Idents(seu) <- "RNA_snn_res.0.6"
ClusterMarkers <- FindAllMarkers(seu, only.pos = TRUE)

```
Have a look at the top cluster markers
```{r}
head(ClusterMarkers)
top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt =avg_log2FC)

top2 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=2, wt =avg_log2FC)
DoHeatmap(seu, features = top5$gene, size = 3, angle = 90, group.by = "RNA_snn_res.0.6")

DoHeatmap(seu, features = top2$gene, size = 3, angle = 90, group.by = "RNA_snn_res.0.6")
```

Now use EnrichR to check cell type libraries

```{r}
setEnrichrSite("Enrichr") # Human genes
# list of all the databases
# get the possible libraries
dbs <- listEnrichrDbs()

# this will list the possible libraries
dbs

# select libraries with cell types
db <- c('CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')

# function for a quick look
checkCelltypes <- function(cluster_num = 0){
  clusterX <- ClusterMarkers %>% filter(cluster == cluster_num & avg_log2FC > 0.25)
  genes <- clusterX$gene
  # the cell type libraries
  # get the results for each library
  clusterX.cell <- enrichr(genes, databases = db)
  # visualize the results
print(plotEnrich(clusterX.cell[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'CellMarker_Augmented_2021'))
print(plotEnrich(clusterX.cell[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'Azimuth_Cell_Types_2021'))

}


```

Check each cluster
```{r}
cluster0 <- checkCelltypes(cluster_num = 0)

```
```{r}
cluster1 <- checkCelltypes(cluster_num = 1)
```

```{r}
cluster2 <- checkCelltypes(cluster_num = 2)
```

```{r}
cluster3 <- checkCelltypes(cluster_num = 3)
```
```{r}
cluster4 <- checkCelltypes(cluster_num = 4)
```
```{r}
cluster5 <- checkCelltypes(cluster_num = 5)
```
```{r}
cluster6 <- checkCelltypes(cluster_num = 6)
```
```{r}
cluster7 <- checkCelltypes(cluster_num = 7)
```
```{r}
cluster8 <- checkCelltypes(cluster_num = 8)
```
```{r}
cluster9 <- checkCelltypes(cluster_num = 9)
```
```{r}
cluster10 <- checkCelltypes(cluster_num = 10)
```
Add cell type annotations

```{r}
# we need to set the identity to rename
Idents(seu) <- "RNA_snn_res.0.6"

# we need to make a vector of the cell type in the same order - in the cluster order

cell_types <- c("NPC", "RG-astro","NPC-astro",
                 "stem cell 1","NPC-proliferating","Neuron",
                 "stem cell 2","endothelial","proliferating",
                 "progenitors-nestin","glia stem cell")
names(cell_types) <- levels(seu)


seu <- RenameIdents(seu, cell_types)
seu <- AddMetaData(object=seu, metadata=Idents(seu), col.name = "CellTypes")

DimPlot(seu, label = TRUE)

Idents(seu) <- "RNA_snn_res.0.6"
cell_types <- c("NPC", "glia","NPC",
                 "stem","NPC","Neuron",
                 "stem","glia","stem",
                 "stem","glia")
names(cell_types) <- levels(seu)
seu <- RenameIdents(seu, cell_types)
seu <- AddMetaData(object=seu, metadata=Idents(seu), col.name = "MainCelltypes")

DimPlot(seu, label = TRUE)


```

See cell counts for each line and cell type

```{r}
cdf <- as.data.frame(table(seu$souporcell_assignment,seu$CellTypes))

cdf2 <- as.data.frame(table(seu$souporcell_assignment,seu$MainCelltypes))


write.csv(cdf, "/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/soup_ADHD_cellcountslinetype.csv")

df_wide <- cdf %>%
  pivot_wider(names_from = Var2, values_from = Freq)

write.csv(df_wide, "/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/soup_ADHD_cellcountslinetype2.csv")

df_wide2 <- cdf2 %>%
  pivot_wider(names_from = Var2, values_from = Freq)


```

```{r}

Idents(seu) <- "souporcell_status"
table(seu$souporcell_status)
singlet <- subset(seu, idents = "singlet")

```


```{r}
VlnPlot(singlet, features = "nFeature_RNA", group.by = "souporcell_assignment")

Idents(singlet)<- "MainCelltypes"
DimPlot(singlet, group.by = "souporcell_assignment", label = TRUE)

```

We might need to separate and harmonize each cell type.

```{r}
# make a list of seurat objects by our cell type variable
sublist <- SplitObject(singlet, split.by = "souporcell_assignment")
# normalize and find variable features
for (i in 1:length(sublist)){
  sublist[[i]] <- NormalizeData(sublist[[i]], verbose = FALSE)
  sublist[[i]] <- FindVariableFeatures(sublist[[i]], selection.method = "vst")
}



```

```{r}
# Create an empty Seurat object to store the integrated data
# Take the first Seurat object from the list as the starting point
integrated_seurat <- subset(sublist[[1]])


# Iterate over the list of Seurat objects
for (i in 1:length(sublist)) {
  # Rename the 'orig.ident' metadata inside the seurat object to match the object name in the list
  sublist[[i]]$orig.ident <- names(sublist)[i]

}

sample.list <- sublist
for (i in 1:length(sample.list)) {
  # Normalize and scale the data
  sample.list[[i]] <- NormalizeData(sample.list[[i]], verbose = FALSE)
  sample.list[[i]] <- ScaleData(sample.list[[i]], verbose = FALSE)
  # Find variable features
  sample.list[[i]] <- FindVariableFeatures(sample.list[[i]], selection.method = "vst")
  # Get the variable features
  variable_features <- VariableFeatures(sample.list[[i]])
  # Run PCA with the variable features
  sample.list[[i]] <- RunPCA(sample.list[[i]], verbose = FALSE, npcs = 30, features = variable_features)
}

int.anchors <- FindIntegrationAnchors(object.list = sample.list, dims = 1:30, reduction = "rpca")
integrated_seurat <- IntegrateData(anchorset = int.anchors,  dims = 1:30, k.weight = 72)
# the k value is the problem
# must set the k weight to the lowest cell count


```

```{r}

DefaultAssay(integrated_seurat) <- "integrated"
integrated_seurat <- ScaleData(integrated_seurat, verbose = FALSE)
# only the integrated features will be the pca input

integrated_seurat <- RunPCA(integrated_seurat, npcs = 20, verbose = FALSE)
integrated_seurat <- RunUMAP(integrated_seurat, reduction = "pca", dims = 1:20, n.neighbors = 81)


```

```{r}
output_path <- "/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/"

saveRDS(integrated_seurat,paste(output_path,"IntegratedSeuratGraphs.RDS",sep = ""))

```

```{r}

integrated_seurat <- FindNeighbors(integrated_seurat, dims = 1:20, k.param = 81)
integrated_seurat <- FindClusters(integrated_seurat, resolution = c(0.6,1) )

```
```{r}
DimPlot(integrated_seurat, group.by = "orig.ident")
#much better integration
```
```{r}
#integrated_seurat$integrated_snn_res.0.6
DimPlot(integrated_seurat, group.by = "integrated_snn_res.0.6", label=TRUE)
DimPlot(integrated_seurat, group.by = "integrated_snn_res.1", label = TRUE)

```

Now cells per cluster

```{r}
table(integrated_seurat$integrated_snn_res.0.6, integrated_seurat$orig.ident)

table(integrated_seurat$integrated_snn_res.1, integrated_seurat$orig.ident)
```

Which cells match to which subject?

From Michaels correlation:
```{r}
code.tb <- read.table(file = "/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/Genotype_ID_key.txt", 
header = TRUE, sep = "\t")

class(code.tb)

seu <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/IntegratedSeuratGraphs.RDS")

saveRDS(seu, "/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/IntegratedSeuratGraphs.RDS")

```
```{r}

# add the assigned lines into the seurat object
# Assuming your data frame is named 'lookup_df' and has columns 'Genotype_ID' and 'Cluster_ID'
lookup_table <- code.tb[, c("Cluster_ID", "Genotype_ID")]
# add the a new metadata slot
seu$Genotype_ID <- lookup_table$Genotype_ID[match(seu$souporcell_assignment, lookup_table$Cluster_ID)]


seu$Genotype_ID <- ifelse(
  seu$souporcell_assignment %in% lookup_table$Cluster_ID,
  lookup_table$Genotype_ID[match(seu$souporcell_assignment, lookup_table$Cluster_ID)],
  "unassigned"
)



unique(seu$Genotype_ID)

```

Now make a table of the assigned cell counts

```{r}

gene.count <- as.data.frame(table(seu$Genotype_ID))

group.gene <- as.data.frame(table(seu$souporcell_assignment, seu$Genotype_ID))

soup.count <- as.data.frame(table(seu$souporcell_assignment))

# Assuming your long table is named 'long_table'
wide_table <- group.gene %>%
  pivot_wider(names_from = Var1, values_from = Freq)

write.csv(wide_table,"/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/SoupoGeneID.csv")

```

